Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs)

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成果类型：

期刊论文

作者：

Liu, Yan*;Yao, Liyun;Xia, Zhenzhen;Gao, Yonggui;Gong, Zhiyong

通讯作者：

Liu, Yan

作者机构：

[Liu, Yan; Gao, Yonggui; Gong, Zhiyong; Yao, Liyun] Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Peoples R China.

[Xia, Zhenzhen] Hubei Acad Agr Sci, Inst Agr Qual Stand & Testing Technol Res, Wuhan 430064, Peoples R China.

[Liu, Yan; Gao, Yonggui; Gong, Zhiyong] Wuhan Polytech Univ, Coll Food Sci & Engn, Key Lab Deep Proc Major Grain & Oil, Minist Educ, Wuhan 430064, Peoples R China.

[Liu, Yan; Gao, Yonggui; Gong, Zhiyong] Wuhan Polytech Univ, Coll Food Sci & Engn, Hubei Key Lab Proc & Transformat Agr Prod, Wuhan 430023, Peoples R China.

通讯机构：

[Liu, Yan] W

Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Peoples R China.

语种：

英文

关键词：

Convolutional neural networks;Edible oils;Near infrared spectroscopy;Two-dimensional correlation spectroscopy

期刊：

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

ISSN：

1386-1425

年：

2021

卷：

246

页码：

118973

DOI：

10.1016/j.saa.2020.118973

基金类别：

To show the differences of sesame oil from different geographical origins, three sesame oils (α, β and γ) from three geographical areas in calibration set of dataset one were selected randomly. Fig. 2 plots the corresponding synchronous and asynchronous 2D correlation spectra, respectively. It can be seen from Fig. 2(a), (b) and (c) that the synchronous 2D correlation spectra of the three samples from different geographical origins are similar. For both peak positions and intensities, the three

机构署名：

本校为第一且通讯机构

院系归属：

食品科学与工程学院

摘要：

Geographical discrimination and adulteration analysis play significant roles in edible oil analysis. A novel method for discrimination and adulteration analysis of edible oils were proposed in this study. The two-dimensional correlation spectra of edible oils were obtained by solvents perturbation and the convolutional neural networks (CNNs) were constructed to analyze the synchronous and asynchronous correlation spectra of the edible oils. The differences for geographical origins of oils or oil types could be amplificated through the networks. For different networks, the layer sequences and t...

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Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs)

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